IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v23y2021i6d10.1007_s10668-020-01016-1.html
   My bibliography  Save this article

Effect of meteorological factors on COVID-19 cases in Bangladesh

Author

Listed:
  • Abu Reza Md. Towfiqul Islam

    (Begum Rokeya University)

  • Md. Hasanuzzaman

    (Begum Rokeya University)

  • Md. Abul Kalam Azad

    (Begum Rokeya University)

  • Roquia Salam

    (Begum Rokeya University)

  • Farzana Zannat Toshi

    (Begum Rokeya University)

  • Md. Sanjid Islam Khan

    (Begum Rokeya University)

  • G. M. Monirul Alam

    (Bangabandhu Sheikh Mujibur Rahman Agricultural University)

  • Sobhy M. Ibrahim

    (King Saud University)

Abstract

This work is intended to examine the effects of Bangladesh's subtropical climate on coronavirus diseases 2019 (COVID-19) transmission. Secondary data for daily meteorological variables and COVID-19 cases from March 8 to May 31, 2020, were collected from the Bangladesh Meteorological Department (BMD) and Institute of Epidemiology, Disease Control and Research (IEDCR). Distributed lag nonlinear models, Pearson’s correlation coefficient and wavelet transform coherence were employed to appraise the relationship between meteorological factors and COVID-19 cases. Significant coherence between meteorological variables and COVID-19 at various time–frequency bands has been identified in this work. The results showed that the minimum (MinT) and mean temperature, wind speed (WS), relative humidity (RH) and absolute humidity (AH) had a significant positive correlation while contact transmission had no direct association with the number of COVID-19 confirmed cases. When the MinT was 18 °C, the relative risk (RR) was the highest as 1.04 (95%CI 1.01–1.06) at lag day 11. For the WS, the highest RR was 1.03 (95% CI 1.00–1.07) at lag day 0, when the WS was 21 km/h. When RH was 46%, the highest RR was 1.00 (95% CI 0.98–1.01) at lag day 14. When AH was 23 g/m3, the highest RR was 1.05 (95% CI 1.01–1.09) at lag day 14. We found a profound effect of meteorological factors on SARS-CoV-2 transmission. These results will assist policymakers to know the behavioral pattern of the SARS-CoV-2 virus against meteorological indicators and thus assist to devise an effective policy to fight against COVID-19 in Bangladesh.

Suggested Citation

  • Abu Reza Md. Towfiqul Islam & Md. Hasanuzzaman & Md. Abul Kalam Azad & Roquia Salam & Farzana Zannat Toshi & Md. Sanjid Islam Khan & G. M. Monirul Alam & Sobhy M. Ibrahim, 2021. "Effect of meteorological factors on COVID-19 cases in Bangladesh," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 9139-9162, June.
    • Handle: RePEc:spr:endesu:v:23:y:2021:i:6:d:10.1007_s10668-020-01016-1
    DOI: 10.1007/s10668-020-01016-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-020-01016-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-020-01016-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    2. Islam, A.R.M.Towfiqul & Shen, Shuang-He & Yang, Shen-Bin, 2018. "Predicting design water requirement of winter paddy under climate change condition using frequency analysis in Bangladesh," Agricultural Water Management, Elsevier, vol. 195(C), pages 58-70.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Roberta Magnano San Lio & Giuliana Favara & Andrea Maugeri & Martina Barchitta & Antonella Agodi, 2023. "How Antimicrobial Resistance Is Linked to Climate Change: An Overview of Two Intertwined Global Challenges," IJERPH, MDPI, vol. 20(3), pages 1-18, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jun, Seung-Pyo & Yoo, Hyoung Sun & Lee, Jae-Seong, 2021. "The impact of the pandemic declaration on public awareness and behavior: Focusing on COVID-19 google searches," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    2. Muddassar Sarfraz & Muhammad Mohsin & Sobia Naseem & Amit Kumar, 2021. "Modeling the relationship between carbon emissions and environmental sustainability during COVID-19: a new evidence from asymmetric ARDL cointegration approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 16208-16226, November.
    3. Sadefo Kamdem, Jules & Bandolo Essomba, Rose & Njong Berinyuy, James, 2020. "Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Zhou, Qingqing & Zhang, Chengzhi, 2021. "Breaking community boundary: Comparing academic and social communication preferences regarding global pandemics," Journal of Informetrics, Elsevier, vol. 15(3).
    5. Rohitash Chandra & Yixuan He, 2021. "Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-32, July.
    6. Crokidakis, Nuno, 2020. "COVID-19 spreading in Rio de Janeiro, Brazil: Do the policies of social isolation really work?," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    7. Roquia Salam & Abu Reza Md. Towfiqul Islam & Shakibul Islam, 2020. "Spatiotemporal distribution and prediction of groundwater level linked to ENSO teleconnection indices in the northwestern region of Bangladesh," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4509-4535, June.
    8. Xin Jing & Jin Seo Cho, 2023. "Forecasting the Confirmed COVID-19 Cases Using Modal Regression," Working papers 2023rwp-217, Yonsei University, Yonsei Economics Research Institute.
    9. Raydonal Ospina & João A. M. Gondim & Víctor Leiva & Cecilia Castro, 2023. "An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil," Mathematics, MDPI, vol. 11(14), pages 1-18, July.
    10. Hemant Bherwani & Saima Anjum & Suman Kumar & Sneha Gautam & Ankit Gupta & Himanshu Kumbhare & Avneesh Anshul & Rakesh Kumar, 2021. "Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5846-5864, April.
    11. Moghari, Somaye & Ghorani, Maryam, 2022. "A symbiosis between cellular automata and dynamic weighted multigraph with application on virus spread modeling," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    12. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    13. Abreu, Paulo & Santos, Daniel & Barbosa-Povoa, Ana, 2023. "Data-driven forecasting for operational planning of emergency medical services," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    14. Bhardwaj, Rashmi & Bangia, Aashima, 2020. "Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    15. Panja, Madhurima & Chakraborty, Tanujit & Nadim, Sk Shahid & Ghosh, Indrajit & Kumar, Uttam & Liu, Nan, 2023. "An ensemble neural network approach to forecast Dengue outbreak based on climatic condition," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    16. Yiannakoulias, Nikolaos & Slavik, Catherine E. & Sturrock, Shelby L. & Darlington, J. Connor, 2020. "Open government data, uncertainty and coronavirus: An infodemiological case study," Social Science & Medicine, Elsevier, vol. 265(C).
    17. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
    18. Ashwin Muniyappan & Balamuralitharan Sundarappan & Poongodi Manoharan & Mounir Hamdi & Kaamran Raahemifar & Sami Bourouis & Vijayakumar Varadarajan, 2022. "Stability and Numerical Solutions of Second Wave Mathematical Modeling on COVID-19 and Omicron Outbreak Strategy of Pandemic: Analytical and Error Analysis of Approximate Series Solutions by Using HPM," Mathematics, MDPI, vol. 10(3), pages 1-27, January.
    19. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    20. Aldila, Dipo & Khoshnaw, Sarbaz H.A. & Safitri, Egi & Anwar, Yusril Rais & Bakry, Aanisah R.Q. & Samiadji, Brenda M. & Anugerah, Demas A. & GH, M. Farhan Alfarizi & Ayulani, Indri D. & Salim, Sheryl N, 2020. "A mathematical study on the spread of COVID-19 considering social distancing and rapid assessment: The case of Jakarta, Indonesia," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:endesu:v:23:y:2021:i:6:d:10.1007_s10668-020-01016-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.